Methods for modeling neurological development and diagnosing a neurological impairment of a patient

ABSTRACT

One variation of a method for modeling neurological development includes: aggregating electroencephalography (EEG) data that comprise multiple EEG signals of each user in a set of users, EEG signals of each user recorded on multiple distinct dates, the set of users comprising a plurality of users of various known neurological statuses; identifying a synchronization pattern trend within the EEG data of the set of users; and correlating the synchronization pattern trend with neurological development within the set of users.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Application serial number16/456,449, filed Jun. 28, 2019, which is a continuation of U.S.Application serial number 13/565,740, filed Aug. 02, 2022, which claimsthe benefit of U.S. Provisional Pat. Application No. 61/514,418, filedAug. 02, 2011, each of which is incorporated in its entirety herein bythis reference.

TECHNICAL FIELD

This invention relates generally to the field of neuroscience, and morespecifically to new and useful methods for modeling neurologicaldevelopment, for diagnosing a neurological impairment of a patient, andfor tagging an EEG signal.

BACKGROUND

The structure or topology of neural networks is a key factor indetermining brain function. “Connectivity disorders,” such as autism,epilepsy, and Schizophrenia, have developmental components and typicallyemerge at specific developmental stages of an individual. Theseconnectivity disorders are thought to result from inappropriatedevelopment of neural network structure, on some scale, in some regionsof the brain. In more specific terms, connectivity disorders may includeover-dense arborization of local neurons, lack of long-rangeconnectivity, or a combination thereof. While this is generally acceptedwithin the scientific community, there is not a noninvasive way tomeasure the connectivity structure of brains.

Thus, there is a need in the field of neuroscience to create a new anduseful methods for modeling neurological development, for diagnosing aneurological impairment of a patient, and for tagging an EEG signal.This invention provides such new and useful methods.

DISCOVERY

Network structure and the time series produced by the network (in theform of electrical signals from neural spiking) are related. Recentadvances in the physics of fractal networks (also known as “scale-free”or “complex” networks) demonstrate that fractal networks will producechaotic time series or electrical signals that carry information aboutthe network structure. This information cannot be extracted withconventional linear analysis methods (e.g., Fourier decomposition, PCA).Rather, this information is “hidden” in the nonlinear characteristics ofthe time series. Computing nonlinear characteristics from EEG signalscontains information about the brain’s network structure that can beused to discern abnormalities. If the abnormalities are distinctlyassociated with neurological impairments (defined by behavioral andcognitive tests), then the abnormalities may serve as biomarkers forthose disorders.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flowchart representation of a method of a embodiment;

FIG. 2 is a flowchart representation of one variation of the method;

FIG. 3 is a flowchart representation of a method of a second preferredembodiment;

FIG. 4 is a flowchart representation of one variation of the method;

FIG. 5 is a flowchart representation of a method of a third preferredembodiment;

FIGS. 6A and 6B are graphical representations of neurological impairmentmodels of users that are of low-risk and with diagnosed neurologicalimpairments, respectively; and

FIG. 7 is a graphical representation of a neural network.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of preferred embodiments of the invention isnot intended to limit the invention to these embodiments, but rather toenable any person skilled in the art to make and use this invention.

The preferred embodiments of the invention include (1) a method formodeling neurological development, (2) diagnosing a neurologicalimpairment of a patient, and (3) tagging EEG signals. Within thisdocument, the phrase “neurological impairment” includes neurologicaldisorders (such as Parkinson’s, Alzheimer’s, epilepsy, and impairmentsdue to stroke and traumatic brain injury), developmental disorders (suchas mental retardation, Autism, epilepsy, ADHD, cerebral palsy andsimilar motor disorders, and speech impairments), and other relateddevelopmental disorders. Furthermore, within this document, “EEG data”or “EEG signal” includes electroencephalography (EEG) data or signalsbut may additionally or alternatively include magnetoencephalography(MEG) or other neuronal electrical activity sensor data or signals.

1. Method for Modeling Neurological Development

As shown in FIG. 1 , a method S100 for modeling neurological developmentincludes: aggregating electroencephalography (EEG) data that includemultiple EEG signals of each user in a set of users in Block S110, EEGsignals of each user recorded on multiple distinct dates, the set ofusers including multiple users of various known neurological statuses;identifying a synchronization pattern trend within the EEG data of theset of users in Block S120; and correlating the synchronization patterntrend with neurological development within the set of users in BlockS130.

The method S100 preferably functions to collect multiple EEG signalsfrom multiple users, each user supplying at least two EEG signalsrecorded on two separate dates and to correlate time-stamped EEG signalfeatures with neurological development of the users. The data may becollected from individuals participating in standardized activities orsubject to similar or identical stimuli, or may be derived from EEGrecordings during ordinary activities. The users are preferablyindividuals in a large user base, and the neurological status of eachuser in the user base is preferably known, previously diagnosed, orotherwise available. The method S100 preferably collects multiple EEGsignals for each user, wherein EEG signals of one user are recorded onmultiple different days. By extracting EEG features from a large numberof EEG signals across a large (and diverse) user base and thencorrelating the extracted features with neurological development for atleast a subset of users, the method S100 can identify current or changesin neural connectivity of one or more users, current or changes inneurological structure of the one or more users, or markers forneurological impairments of one or more users. The user base preferablyincludes users of various neurological statuses, including usersdiagnosed with a neurological impairment and users not diagnosed withthe neurological impairment. By accessing user neurological statusesthat include neurological conditions, the method S100 can generate a‘neurological impairment model’ of one or more neurological impairmentsbased upon a comparison of trends or features in EEG signals of userswith diagnosed neurological conditions and trends or features in EEGsignals of users without diagnosed neurological conditions. Theneurological impairment model can then be compared with an EEG signal ofa patient to enable detection and/or diagnosis of a neurologicalcondition of the patient, such as in the method S200 described below.

Alternatively, the first preferably method S100 can generate a‘neurological connectivity model,’ a ‘cognitive development model,’and/or a ‘neurological structure model’ by comparing users of similarand/or different neurological statuses, demographics, locations, culturebackgrounds, education, family history, etc. The neurological impairmentmodel, the cognitive development model, the neurological connectivitymodel, and the neurological structure model are preferablytime-dependent models including images of neurological development ofmultiple users over time. However, any one or more of the neurologicalimpairment model, the cognitive development model, the neurologicalconnectivity model, and the neurological structure model can be a singleinstance model, such as including single neurological images of multipleusers. The output of the method S100 can therefore be useful indiagnosing a neurological disease of, setting a teaching curriculum for,identifying strengths and weaknesses of, tracking neurologicaldevelopment of, or informing improved interactions with a user, such asthrough the second preferred method S200.

The method S100 is preferably implemented by a computer system thatcollects EEG signals from EEG sensors worn by users within the userbase. The computer system is preferably cloud-based (e.g., Amazon EC3),but may be a mainframe computer system, a grid-computer system, or anyother suitable computer system. EEG data are preferably collected by thecomputer system over a computer network, such as the Internet. Thecomputer system preferably includes one or more processors configured toanalyze EEG data and one or more data storage modules configured tostore EEG data and a neurological impairment or other model.Furthermore, the computer system is preferably accessible by any of auser, a doctor, a patient, an employer, a teacher, a spouse, a familymember, a psychiatrist, a therapist, a military agent, an insuranceagency, etc. to inform future interactions with or medical diagnoses ofa user or patient. EEG data, neurological development, the neurologicalimpairment model, etc. are preferably accessible through a web browseror native application executing a digital multimedia device, such as alaptop computer, a desktop computer, a tablet, a smartphone, a personaldata assistant (PDA), a personal music player, or a hospital server,though the EEG data, neurological development, the neurologicalimpairment model, etc. can be accessed in any other way, through anyother suitable device, and/or by any other entity. Furthermore the localcomputer or other data collection device may be configured to deliverstandardized stimuli or activities to the user with a means to time-locksaid stimuli or activities to the EEG data recording system for lateranalysis and development of the neurological impairment model.

Block S110 of the method S100 recites aggregating EEG data that comprisemultiple EEG signals of each user in a set of users, EEG signals of eachuser recorded on multiple distinct dates, the set of users comprising aplurality of users of various known neurological statuses, with orwithout the participation of the user in standardized activities orstimuli. Each EEG signal is preferably recorded through a neuroheadsetworn by a user in the user base. For example, each user can record anEEG signal through an Emotiv EPOC neuroheadset, a neuroheadset describedin U.S. Publication No. S2007/0066914 and in U.S. Publication No.S2007/0173733 (both of which are incorporated in their entirety byreference), or any other suitable EEG sensor, electrode, or headset.Each neuroheadset preferably includes multiple electrodes (e.g.,fourteen electrodes, nineteen electrodes) that, when worn, contactcertain regions of the scalp of a user. Each electrode preferably senseselectrical disturbances along the scalp of the user, such as a voltagechange across two electrodes (e.g., across a sense electrode and aground or reference electrode). Each EEG signal therefore preferablyincludes multiple subsignals, each subsignal output by at least oneelectrode of a neuroheadset and representing an electrical disturbanceproximal an associated electrode or across two electrodes. Thearrangement of each electrode is also preferably predefined or knownsuch that each subsignal can be correlated with user neuronal activityin a region of the brain proximal the associated electrode. Furthermore,each neuroheadset is preferably sized for or adjustable to fit a usersuch that the neuroheadset can be repeatedly adorned and removed by theuser with each electrode in the headset retained against substantiallythe same area of the scalp with each subsequent application.

Neuroheadsets (or other sensors, etc.) worn by the users to record EEGsignals preferably define a distributed network of EEG sensors.Generally, the distributed network preferably includes a large number ofneuroheadsets, each worn by at least one user in the user base andpreferably defining a network node. The user base is preferablysubstantially large, such as including hundreds, thousands, or millionsof users, and the distributed network is therefore also substantiallylarge, such as including hundreds, thousands, or millions of nodes(e.g., neuroheadsets). Each user in the user base preferably takesmultiple EEG recordings over the course of several days, weeks, oryears, and each EEG recording is preferably communicated to the computersystem, wherein each EEG signal is aggregated with previously-stored EEGsignals of both a respective user and other users in the user base.

Additionally or alternatively, magnetic field signals from an MEGmachine or images from a CAT, MRI, or any other sensor, machine, orimaging system capable of recording neurological activity of a user canbe aggregated, analyzed, and correlated via the method S100. However,the EEG sensors or other devices that capture neurological states orneurological activity of the users in the user base are preferablyaccessible to the users in residential and/or commercial settings ratherthan solely in medical or clinical settings, thus enabling users torecord EEG signals at various times, in various environments, whileengaging in various activity, and/or while experiencing various moods oremotion states. The method S100 can therefore aggregate a colorfuldataset of EEG signals corresponding to a wide variety of users ofwidely varying demographic, neurological condition, personality,activity, mood, emotion etc., collected either in normal activity orassociated with standardized activities or stimuli which may enablegreater insight into neuronal development and neurological conditions. Aneurological impairment model generated through analysis of the colorfuldataset may further improve accuracy and speed of neurological diagnoseswhen implemented in the method S200.

Each EEG signal is preferably recorded over a period of time andtherefore represents a time-lapse of electrical activity across aportion of the scalp of a user. For example, an EEG signal can be twentyseconds, three minutes, or an hour long, or of any other length. EEGsignals can be recorded at set times for a user, such as 10AM PST everyMonday for a year, can be recorded during specific events, such as whilea user is eating dinner every weekday for two months, or can be recordedsubstantially haphazardly, such as whenever a user is inclined to recordan EEG signal.

At least some EEG signals are preferably tagged with a neurologicalstatus of respective users. A neurological status tag includes anyconcentration, focused or engaging task/activity, cognitive or mentaltask/activity or standardized activity or stimulus. A neurologicalstatus tag preferably also includes any diagnosed neurological disorder(e.g., Parkinson’s, Alzheimer’s, epilepsy), developmental disorder(e.g., mental retardation, Autism, epilepsy, ADHD, cerebral palsy), orcognitive disorder (e.g., amnesia, dementia), though the neurologicalstatus tag can also include a motor disorder, speech impediment,depression, addiction, or any other current or previous disorder ordiagnosed medical condition. Generally, the neurological status tagspreferably enable correlation of EEG signal features with particularneurological impairment in Block S130.

Because various user activities may activate different portions of thebrain and to various degrees, neuronal electrical activity may beaffected to some varying degrees with an activity or state of the userduring EEG recordation. Each EEG signal is therefore preferably taggedwith situational information pertaining to a respective user during EEGsignal collection. This situational information can be any one or moreof a location of the user, an environment around the user, a userbiometric, a user activity or activity detail, a mood or emotion of theuser, media content viewed by the user, or any other relevantuser-related information contemporaneous with recordation of an EEGsignal including standardized content or stimuli delivered over the datacollection network. Situational information can be generated or capturedautomatically or entered manually by the user. In one exampleimplementation, a neuroheadset transmits an EEG signal to a smartphonecarried by a user, and a native application executing on the smartphonetags the EEG signal with one or more outputs from a GPS sensor, anaccelerometer, a gyroscope, a camera, and/or a microphone incorporatedinto the smartphone. In this example implementation, when the userinteracts with the smartphone during EEG signal recordation, contentrendered on and/or user interactions with the smartphone can also betagged to the EEG signal. In another example implementation, outputs ofa blood oximeter, a heart rate sensor, an EKG machine, a respiratorysensor, a blood pressure sensor, and/or any other biometric sensor canbe transmitted to the computer system or to a digital multimedia device(e.g., a smartphone) of a user, wherein one or more such biometricoutputs is tagged to a contemporaneous user EEG signal. In a furtherexample implementation, a calendar entry, email or telephonycommunication, television guide, a native application executing on asmartphone or tablet, a thermostat thermometer, floor traffic sensor, orany other suitable user-related or environmental sensor or outputinforms a situational tag associated with an EEG signal. In anotherexample implementation, situational information is entered manually bythe user, such as with a note or survey entered or completed through atouch screen or a web-based interface once recordation of an EEG signalis complete. For example, an EEG signal of a user can be tagged with thelocation of the user as determined through a GPS sensor of a smartphonecarried by the user, tagged with content rendered on a display of thesmartphone during EEG recordation, tagged with a time and date of theEEG signal as maintained by the smartphone, and tagged with a mood andemotion of the user entered through a touch screen on the smartphonebefore, during, and/or after the smartphone records an EEG signalthrough a wired or wireless connection to a neuroheadset worn by theuser. The EEG signal can additionally or alternatively be tagged with acognitive or mental user exercise or activity (e.g., a brain exercisesor brain training task), a task that requires user focus or engagement(e.g., learning a new language, playing a musical instrument), aphysical exercise or activity that is new to the user (e.g., learning tothrow a baseball), or any other activity, detail, or information relatedto the EEG signal. In another implementation, the user can be presentedwith a series of standardized stimuli such as questionnaires, still andvideo images and/or audio sequences, games or interactive activitieschosen to elicit certain reactions, and the EEG data stream istime-stamped with markers indicating the exact moment of delivery ofeach successive stimulus. This data can be analyzed for each user oraggregated over large groups of users of known or proposed neurologicalstatuses.

As shown in FIG. 2 , a variation of the method S100 can include BlockS150, which recites determining an action of the user based upon theoutput of a physiological or environmental sensor. The physiological orenvironmental sensor is preferably proximal a user during recordation ofan EEG signal and is preferably configured to sense an environmentalcondition or a condition of a user. For example, the physiological orenvironmental sensor can be an accelerometer incorporated into a neuralheadset, a GPS sensor in a cellular phone carried by the user, athermometer or thermostat in a room occupied by the user, or any or anyother aforementioned or suitable sensor. By analyzing a sensor output,Block S150 preferably estimates an action performed by a user while anEEG signal of the user is recorded, and this estimated action ispreferably tagged to its contemporaneous user EEG signal. For example apulse oximeter outputting a high user respiratory rate and anaccelerometer outputting sinusoidal vertical oscillations in the rangeof 1.2-1.6 Hz can suggest that the user is jogging, and a jogging tag isadded to a contemporaneous user EEG signal in Block S150. Similarly, apulse oximeter outputting a low user respiratory rate and anaccelerometer indicating little or no user movement can suggest that theuser is sleeping or resting, and a sleeping or resting tag is added to acontemporaneous user EEG signal in Block S150. In another example, a GPSsensor output that identifies the user at a first coordinate canindicate the user is at work while a GPS sensor output that identifiesthe user at a second coordinate can indicate the user is at home, and anactivity commonly associated with those locations (e.g., working,sleeping) is added to a contemporaneous user EEG signal in Block S150.Block S150 can additionally or alternatively implement any of machinelearning, machine vision, object recognition, audio transcription orvoice detection, or any other suitable technique to extrapolate relevantuser or environmental information from the sensor to determine an actionof the user during EEG recordation. However, sensor data can be analyzedin any other way to extract relevant user situational information.

Alternatively, as shown in FIG. 2 , another variation of the method S100includes Block S140, which recites directing a user to perform aspecified action during recordation of an EEG signal and tagging the EEGsignal with the specified action. Generally, Block S140 preferablyhandles transmission of an action directive for the user, such as adirective to eat, rest, exercise, read a book, watch a television show,or work. The directive is preferably transmitted to the user in the formof a notification accessible through a native application executing on adigital multimedia device carried by the user. Alternatively, thedirective can be communicated to the user in the form of an email, SMStext message, calendar update or alert, voicemail, or any other suitableform of communication. The directive can additionally or alternativelyinclude a preferred time to perform an action and a reminder to wear aneuroheadset while performing the action. Details of the directivepresented to the user preferably further inform a tag that is associatedwith an EEG signal recorded while the user performs or is expected toperform an action noted in the directive. For example, Block S140 cancommunicate to a user a directive to make an EEG recording while walkinghis dog between 6:30 and 7:00 the following morning. In anticipation ofthe user performing the desired action, Block S140 tags an EEG recordingcompleted and uploaded to the computer system at 7:04 on the followingmorning with the ‘walking dog’ action tag as noted in the directive.Generally, a directed action is preferably tagged to its anticipatedcontemporaneous user EEG signal in Block S140. Block S140 canadditionally or alternatively handle distribution of a visual, audible,or haptic stimulus to a user prior to or during EEG signal recordation.Details of a stimulus are preferably tagged to a contemporaneous userEEG signal. However, Block S140 can function in any other way.

Additionally or alternatively, a user can manually enter an action orenvironmental tag to be paired with an EEG signal. In thisimplementation, once a user completes an EEG recording, he can access aweb browser or native application executing on a smartphone or computerto enter details of the recording, such as where and when the signal wasrecorded as well as what the user was doing, an environment proximal theuser, and/or a user mood, emotion, or feeling during EEG recordation. Inone example implementation, the user complete an online survey once anew EEG signal is uploaded to the computer system, and the computersystem tags the EEG signal with relevant data based upon the survey.However, a user can provide any other suitable detail in any other way,and the method S100 can extract any other suitable information from auser input to generate a tag for a user EEG signal.

Each EEG signal is also preferably tagged with personal or demographicinformation of a respective user. For example, the age, gender,handedness, occupation, hobby, interest, education level, spokenlanguage(s), familial history, and/or cultural background can be notedwith an EEG signal of each user. Generally, situational and/or personaluser information preferably identifies inputs, environmental influences,internal factors (e.g., mood, emotion), and/or human characteristicsthat may affect user neuronal activity. However, an EEG signal can betagged with any other suitable neurological impairment, situationalinformation, timing of standardized activities or stimuli, or personalinformation of a respective user to improve correlation of EEG signalswith a neurological impairment in Block S130.

EEG signals captured by neuroheadsets across the distributed network andany relevant neurological, situational, and/or personal tags arepreferably transmitted to the computer system via wired or wirelessconnections with Internet-accessible digital multimedia devices.Furthermore, EEG signals and any relevant tags are preferably encryptedand/or anonymized such that data transmission between a local userdevice and a computer system conforms to all relevant user and patientprivacy regulations (e.g., HIPAA). EEG signals and/or tags arepreferably encrypted according to a cryptographic protocol such asDiffie-Hellman key exchange, Wireless Transport Layer Security (WTLS),or any other suitable type of protocol. EEG signals can also beencrypted according to encryption standards such as the Data EncryptionStandard (DES), Triple Data Encryption Standard (3-DES), or AdvancedEncryption Standard (AES).

Block S120 of the method S100 recites extracting a synchronizationpattern trend from the EEG data. Generally, Block S120 preferablyanalyzes EEG signals from users to identify interacting (nonlinear)dynamical systems that describe neuronal activity and therefore brainfunction and neural connectivity of the users. Block S120 thereforepreferably functions to reveal information pertaining to a human neuralnetwork structure that is otherwise “hidden” in characteristics of EEGtime-series signals.

Block S120 preferably includes comparing synchronization patternsemerging from multiple EEG signals of a particular user and recorded onmultiple distinct dates to identify a synchronization pattern trend ofthe user. Generally, Block S120 preferably compares two EEG signalsrecorded on different dates but while the user was engaged insubstantially the same or similar activities. Block S120 can thusisolate synchronization patterns that are common to a particular task oractivity for the user from synchronization patterns that are erroneous,haphazard, or only loosely correlated with the user task or activity. Byidentifying synchronization patterns common to a particular user task oractivity, Block S120 can filter out synchronization patterns that may besignificantly affected by factors substantially unrelated to the useractivity or task, such as user stress level, user mood, user energylevel, or a past or upcoming task. This can significantly increase theaccuracy and/or precision of a correlation between an extrapolated usersynchronization pattern trend and one or more neurological conditions,as in Block S130.

Block S120 can similarly compare synchronization patterns of EEG signalsof a user recorded at various times or during various tasks oractivities to identify user neuronal activity not tied to a specificaction, activity, task, or brain function. Certain synchronizationpatterns or EEG signal features correlated with this neuronal activitycan then be filtered out of the EEG data as noise, parasympathetic brainfunction, etc. Alternatively, this neuronal activity can be used to mapbasic neural connectivity in the brain of the user. For example,substantially constant neuronal activity can be compared to changes inneuronal activity over time to identify which portion of the brain ofthe user are and are not developing.

In one example implementation of the method S100, Block S120 implementsfast Fourier transform (FFT) computation to determine synchrony betweenfrequency bands of an EEG signal. In this example implementation, BlockS120 preferably includes filtering an EEG signal of a user, comparingtwo filtered bands of the EEG signal through spectral analysis, andextracting stable phase-difference (e.g., decoupling) or phase-locking(e.g., coupling) episodes between the two signal bands via statisticalidentification of phase-locking synchrony. By similarly analyzing EEGsignals from other users, the preferred method can aggregate a set ofphase-difference or phase-locking episodes, wherein features of theepisodes (e.g., frequency, magnitude, duration, neuronal location) canbe correlated with a neurological impairment in Block S130, such asbased upon users with a similar neurological condition who exhibitsimilar phase-difference episodes distinct from phase-differenceepisodes of users without the neurological condition. Additionally oralternatively, Block S120 can include filtering an EEG signal of a firstuser and an EEG signal of a second user, comparing filtered bands of theEEG signals through spectral analysis, and extracting stablephase-difference episodes between the two EEG signals via statisticalidentification of phase-locking synchrony. In this exampleimplementation, Block S130 can correlate features of the EEG signals ofthe users with a neurological impairment by isolating phase-differenceepisodes of users with similar neurological conditions fromphase-difference episodes of users with differing neurologicalconditions.

In another example implementation of the method S100, Block S120implements empirical model decomposition. In this exampleimplementation, Block S120 preferably extracts intrinsic modes from anEEG signal and applies a Hilbert transform to each mode to calculatephase synchrony between frequency bands of the EEG signal.

Alternatively, Block S120 can calculate synchrony entropy (e.g., aspatiotemporal variability of synchrony between unstable dynamicalsystems), a Lyapunov exponent, multiscale properties of a time-series, aphase-space reconstruction of a time-series, a multichannelsynchronization parameter, or any other suitable parameter or EEG signalfeature to assess linear or nonlinear coupling between EEG frequencybands and/or between EEG signals, such as through multiscale entropy(MSE) analysis. However, Block S120 can implement any other technique oranalysis to extract relevant features or metrics from one or more EEGsignals.

Block S120 preferably identifies one synchronization pattern trend inone channel (i.e. one subsignal of an EEG signal) pertaining to one ortwo electrodes of a neuroheadset worn by a respective user. Block S120preferably further identifies synchronization pattern trends inadditional channels pertaining to other electrodes of the neuroheadset,thereby outputting multichannel cross-band synchronization patterns.

Through any one or more of the aforementioned implementations ormethods, Block S120 preferably extracts a synchronization pattern trendof a user from the EEG signals of the users. Block S120 can furthercompare EEG signals of multiple users to extract a synchronizationpattern trend within a group of users. Generally, Block S120 preferablyidentifies, from multiple EEG signals recorded on two or more distinctdates, a synchronization pattern trend that is at least one of afeature, a time-series, an entropy curve, a phase-difference episode, aphase-locking episode, a dynamical neuronal subsystem, a neural networkstructure or topology, or an emergent hierarchical neuronal cluster (asshown in FIG. 7 ). The synchronization pattern trend preferablyincorporates a time element correlated with neural development orchanges in neural connectivity for a user over time. Generally, thesynchronization pattern trend of a user can be compared withsynchronization pattern trends of other users in Block S130 to correlatea neurological condition with a synchronization pattern, an EEG signal,and/or neuronal activity.

Block S130 of the method S100 recites correlating the synchronizationpattern trend with neurological development within the set of users.Generally, Block S130 preferably remotely correlates the synchronizationpattern trend with neurological development to generate a neurologicalimpairment model that can be accessed in the method S200 to diagnose aneurological condition of a patient. Alternatively, Block S130 cangenerate a ‘neurological connectivity model’ or a ‘neurologicalstructure model’ by comparing users of similar and differentneurological development or statuses.

As described above, Block S130 preferably compares a synchronizationpattern trend of EEG signals of a first user with a synchronizationpattern trend of EEG signals of a second user and thereby isolatessimilarities and/or differences between the synchronization patterntrends. Neurological condition tags associated with each EEG signal (orrespective user) preferably enable identification of synchronizationpattern similarities between users with similar neurological conditions,as well as identification of synchronization pattern differences ofusers with different neurological conditions. By grouping similaritiesand differences in synchronization pattern trends of users of varyingneurological condition within the user base, a model of synchronizationpattern trends and correlated neurological conditions can beextrapolated from the EEG dataset. Block S130 preferably correlates thesynchronization pattern of at least one EEG channel with a neurologicalcondition, though Block S130 can augment this a correlation ofadditional synchronization patterns of additional EEG channels with thesame or other neurological condition. For example, Block S130 canimplement multichannel cross-band synchronization analysis. Block S130preferably further bolsters or ‘tunes’ the neurological impairment modelwith additional EEG signals (and subsignals) provided over time by usersin the user base.

Block S130 preferably filters comparisons between EEG signals ofdifferent users based upon demographic and/or situational informationassociated with the users. In one example implementation, Block S130compares EEG signals of two users, each recorded while the users wereperforming similar actions, such as reading, walking, sleeping, orworking. In this example implementation, Block S130 also selectivelyexcludes comparison of EEG signals recorded while respective users wereengaged in dissimilar activities, such as reading and exercising, eatingand sleeping, or working and talking with a friend. In another exampleimplementation, Block S130 compares EEG signals of two users of asimilar demographic, such as same age group, same gender, or similaroccupation, and Block S130 further selectively excludes comparison ofEEG signals of two users of a dissimilar demographic. However, BlockS130 can compare EEG signals according to any other similar ordissimilar factor, demographic, or environmental condition. By groupingEEG signals (or their respective extracted synchronization patterns)according to demographic, environmental, and/or personal information,Block S130 can minimize errors in derived corollaries between EEGsynchronization patterns and neurological conditions.

Block S130 preferably extracts baseline EEG data from each user suchthat other users actions, mental states, etc. can be compared with thebaseline EEG data to enable correlation of neural activity withparticular user actions, mental states, standardized stimuli, etc. BlockS130 preferably tags EEG signals recorded while the user is sitting witheyes open and/or eyes closed while breathing softly but regularly as abaseless EEG signals, as these signals may capture a minimal amount ofbrain activity. For example, Block S130 can subtract a baseline user EEGsignal from an EEG signal recoded while the user was reading a novel togenerate a composite EEG signal increased user brain activity associatedwith reading. Block S130 can thus compare EEG baseline signals with EEGsignals associated with other particular user actions or states toisolate user neuronal activity unique to a subset of user actions ormental states.

Block S130 preferably also filters comparisons between EEG signals ofdifferent users based upon neurological conditions of the users. In oneexample implementation, Block S130 constructs a baseline neurologicalimpairment model by comparing synchronization patterns of a first set ofusers with no known neurological impairments with (1) synchronizationpatterns of a second set of users with a first diagnosed neurologicalimpairment but no other significant neurological impairment and (2)synchronization patterns of a third set of users with a second diagnosedneurological impairment but no other significant neurologicalimpairment. Block S130 can further repeat this comparison for additionalsets of users with other neurological impairments. This baseline,single-dimension (i.e. single neurological impairment) correlationbetween brain activity and neurological impairment can thus define afoundation for identifying EEG signal synchronization patterncharacteristics indicative of single neurological impairments in futureusers or patients. In this example implementation, once the baseline isconstructed, synchronization patterns of users with more complexneurological impairments, such as two or more diagnosed impairments, canbe compared with synchronization patterns of users with fewer or nodiagnosed neurological impairments to construct a multi-dimensionalmodel of neurological impairments and correlated synchronizationpatterns. Multiple neurological impairments may affect neuronal activityand therefore synchronization patterns in non-linear ornon-combinatorial ways, and Block S130 can therefore identify (unique)synchronization patterns that correlate with neural interactions betweenmultiple neurological conditions. Block S130 can therefore generate aneurological impairment model including catalogued synchronizationpattern ‘fingerprints’ that can be compared with synchronizationpatterns and/or EEG signals of future users or patients to identity anddiagnose one or more neurological impairments. The neurologicalimpairment model preferably accounts for user demographic, activity,location, mood, emotion etc., environmental conditions, time,repetition, etc. by grouping and comparing synchronization patterntrends according to one or more of these details. For example, BlockS130 can generate a neurological impairment model including a controlmodel graphically represented as mean entropy curves of low-risk users(shown in FIG. 6A) and a high-risk model graphically represented as meanentropy curves of users with a particular diagnosed neurologicalimpairment (shown in FIG. 6B).

Block S130 can further implement machine learning to tag and clustersynchronization patterns of certain EEG signals. Block S130 can alsoimplement machine learning when augmenting an existing neurologicalimpairment model with a recent remote patient diagnosis, such asgenerated through the method S200.

In an example implementation of the method S100, Block S120 includescalculating synchronization patterns that include synchronous bandoscillations within an EEG signal, and Block S130 includes extractingneurological function of a user from these synchronous bandoscillations. For example, synchronous gamma oscillations can becorrelated with temporary representation of complex objects in userworking memory and/or a mechanism by which to tie brain regions involvedin associative learning into Hebbian cell assemblies. Localsynchronization in the theta band can be correlated with informationencoding and retrieval in episodic memory. Theta band coupling betweenfrontal and post rolandic cortical regions can be further correlatedwith a retention interval of visual working memory tasks and/or anN-back working memory task. Stronger theta band coherence can becorrelated with a higher intelligence. Local desynchronization in thelower alpha band can be correlated with attentional processes, and upperalpha band desynchronization can be correlated with semantic memory.Block S130 can thus identify neuronal connectivity pathways acrossportions of the brain of a user, which can be forward or reverse linkedto a neurological impairment or condition. Therefore cross-bandnonlinear (or linear) synchronization pattern trends across particularchannels can be correlated with particular neural functions, and BlockS130 can aggregate these patterns, channels, and neural functions togenerate and grow the neurological impairment model.

As shown in FIG. 2 , one variation of the method S100 further includesBlock S160, which recites mapping a first neural connectivity of a userbased upon a first EEG signal of the user recorded on a first date. Asdescribed above, an EEG signal preferably includes multiple subsignalsoutput by multiple (e.g., fourteen, nineteen) electrodes incorporatedinto a neuroheadset. By analyzing and comparing the EEG subsignalscorrelating with brain activity, Block S160 can map clusters ofconnected neurons and neuronal pathways in a user’s brain, such asgraphically represented in FIG. 7 . Block S160 can further analyze EEGsignals recorded at other distinct times or during other user activitiesto generate and/or improve a neural network map of connectionsthroughout the user’s brain. For example Block S160 can aggregate userEEG signals tagged with various user activities, such as sleeping,eating, working, exercising, reading, and conversing, to identify neuralconnections as each portion of the brain is exercised.

In this variation of the method S100, identifying the synchronizationpattern trend in Block S120 can further include identifying changes inneural connectivity of the user by comparing a first neural connectivityassociated with a first date with a second neural connectivity of theuser based upon EEG subsignals of a second EEG signal recorded on asecond (later) date. Known or diagnosed brain development conditions ofthe user can then be correlated with identified changes in neuralconnectivity over time. For example, for a user diagnosed with autism,user neural connectivity maps may depict growing clusters of localconnectivity but without longer-range connections between clusters, andthis neural development (i.e. changes in neural connectivity) can becorrelated with a high risk for autism.

In an example implementation, the method S100 includes: collectinginformation over a distributed network based upon EEG signals of userswith a neurological impairment; collecting information over adistributed network based upon EEG signals from users without adiagnosed neurological impairment; computing nonlinear synchronizationpatterns from the information; and correlating the nonlinearsynchronization patterns with neurological conditions based upon userswith a (diagnosed) neurological impairment and on users without a(diagnosed) neurological impairment. In this example implementation,collection of EEG signals is split between users with and without aneurological impairment. Generally, in this example implementation, themethod S100 can specifically target users with and without a knownneurological impairment. Alternatively in this example implementation,the method S100 can randomly target users and subsequently inquire abouta user’s mental status or neurological condition.

In another example implementation, the method S100 includes: receivinginformation over a distributed network based upon a series of EEGsignals from users with a neurological impairment; receiving informationover a distributed network based upon a series of EEG signals from userswithout a neurological impairment; computing nonlinear synchronizationpatterns from the information; and correlating the nonlinearsynchronization patterns based upon the users with a neurologicalimpairment and on the members without a neurological impairment.Compared with the first specific example, the step of receivinginformation now includes receiving information based upon a series ofEEG signals. The series of EEG signals preferably includes a series ofEEG signals recorded over a substantially long time period, such as on adaily basis for several months or on a weekly basis for several years.The extended series of EEG signals preferably facilitates correlation ofuser development of a neurological impairment. For a particular user,the series of EEG signals is preferably collected by the same device (orat least the same type of device with the same settings) and undersimilar situations (or at least under a categorized situations basedupon situation information, as described above).

In a further example implementation, the method S100 includes: receivinginformation over a distributed network based upon EEG signals recordedduring a time period associated with a user epileptic seizure; receivinginformation over a distributed network based upon EEG signals during atime period not associated with a user epileptic seizure; computingnonlinear synchronization patterns from the information; and correlatingthe nonlinear synchronization patterns based upon the time periods withan epileptic seizure and on the time periods without an epilepticseizure. This information can subsequently be aggregated to generate theneurological impairment model that includes an epilepsy model. However,the method S100 can be implemented or applied in any other way.

2. Facilitating the Diagnosis of a Neurological Impairment of a Patient

As shown in FIG. 3 , a method S200 for facilitating the diagnosis of aneurological impairment of a patient includes: aggregating multipleelectroencephalography (EEG) signals of the patient in Block S210, eachEEG signal recorded on a distinct date; identifying a synchronizationpattern trend in the EEG signals of the patient in Block S220; comparingthe synchronization pattern trend of the patient with a neurologicalimpairment model comprising a correlated neurological impairment inBlock S230; and diagnosing the patient with the neurological impairmentbased upon the comparison in Block S240.

The method S200 preferably functions to diagnose the patient with theneurological impairment based upon a comparison of trends in EEG signalsor features thereof with a neurological impairment model, such as theneurological impairment model generated by the method S100. Theneurological impairment is preferably autism, epilepsy, Schizophrenia,or Alzheimer’s disease, but may alternatively be any other neurologicaldisorder, developmental disorder, cognitive disorder, etc.

As with the method S100, the method S200 preferably executes on acomputer system that collects multiple EEG signals of a patient,extracts relevant features or information from the EEG signals (e.g., asynchronization pattern trend), and compares the extracted feature witha neurological impairment model to facilitate the diagnosis aneurological condition of the patient. Furthermore, the computer systemthat implements the method S100 can be the same computer system thatimplements the method S200. Alternatively, a first computer system canimplement the method S100 while a separate second computer systemimplements the method S200, wherein the second computer system accessesthe neurological impairment model of the method S100 from the firstcomputer system. For example, the first computer system can be a remoteserver associated with a hospital network, and the second computersystem can be a local server specific to a local hospital, wherein aphysician at the local hospital initiates a patient diagnosis via themethod S200 via the local server that accesses the remote server.Alternatively, the method S200 can execute on a digital multimediadevice local to the patient. For example, the patient can couple aneuroheadset to his smartphone and record an EEG signal on thesmartphone through the neuroheadset, wherein the smartphone accesses theneurological impairment model (e.g., downloads the model from a computersystem) and locally diagnoses the patient by comparing synchronizationpatterns of the patient with the neurological impairment model. However,the method S200 can function in any other way and execute on any othersystem, server, or device.

As with the method S100, the method S200 preferably manipulates EEGsignals recorded through an EEG neurological headset worn by thepatient, such as in a residential or commercial setting rather thatsolely a medical or clinical setting. Each EEG signal also preferablyincludes multiple EEG subsignals output by multiple electrodes of aneuroheadset. However, an MEG signal or any other suitable neurologicalimage can be manipulated by the method S200.

As with the method S100, each EEG signal of the method S200 ispreferably tagged with personal, demographic, environmental, and/orneurological information pertaining to the patient and/or theenvironment proximal the patient while the EEG signal is recorded. Asdescribed above, any of this personal or situational information can becollected through a physiological or environmental sensor proximal thepatient during EEG signal recordation, such as an accelerometerincorporated into a neuroheadset worn by the patient or a GPS sensorincorporated into a cellular phone carried by the patient.

Block S210 of the method S200 recites aggregating multiple EEG signalsof the patient over multiple dates. Block S210 preferably collects EEGsignals of the patient in a manner similar to Block S110 of the methodS100.

Block S220 of the method S200 recites identifying a synchronizationpattern trend in the EEG signals of the patient. Block S220 preferablyidentifies the synchronization pattern trend in the EEG signals of thepatient in a manner similar to Block S120 of the method S100. Generally,Block S220 can extract the synchronization pattern of the patient viaone or more channels of patient EEG signals recorded on multiple dates.The synchronization pattern trend is preferably at least one of afeature, a time-series, an entropy curve, a phase-difference episode, aphase-locking episode, a dynamical neuronal subsystem, a neural networkstructure or topology, an emergent hierarchical neuronal cluster (asrepresented in FIG. 7 ), or a multichannel cross-band nonlinearsynchronization pattern. This synchronization pattern trend of thepatient is then preferably compared with the neurological impairmentmodel of the method S100 to diagnose a neurological condition of thepatient in Blocks S230 and S240, respectively.

Block S230 of the method S200 recites comparing the synchronizationpattern trend of the patient with a neurological impairment modelcorrelated with a neurological impairment. Block S230 preferablycompares the synchronization pattern trend in the EEG signals of thepatient with the neurological impairment model in a manner similar tothe methods and techniques implemented in Block S120 of the method S100to correlate the synchronization pattern trend of the user with aneurological impairment.

Similar to the method and techniques described above, Block S230preferably filters the neurological impairment model to selectparticular submodels for comparison with the synchronization patterntrend of the patient. Through filtering, Block S230 can pair a patientsynchronization pattern trend with model synchronization pattern trendsof users most similar to the patient or model synchronization patterntrends best fit to the patient. For example, Block S230 can filter thesynchronization model according to patient demographic, situationalinformation, patient activity, patient mood, patient location, or anyother suitable personal, environmental, or external factor orinformation. Block S230 can implement thresholds, least mean squares,K-nearest neighbors, or any other parametric or non-parametricextrapolation to correlate a patient synchronization pattern trend witha neurological condition noted in the neurological impairment model.However, Block S230 can function in any other way.

In one example implementation of the method S200, Block S220 includescalculating synchronization patterns that include synchronous bandoscillations within a patient EEG signal, and Block S230 includesdetermining neurological function of the patient based upon a comparisonof these synchronous band oscillations with the neurological impairmentmodel that also includes synchronous band oscillations.

Block S240 of the method S200 recites diagnosing the patient with theneurological impairment based upon the comparison. Generally, Block S240preferably generates a diagnosis for the patient by assigning aneurological condition or status to the patient based upon an output ofBlock S230. Generally, the recommended diagnosis of a neurologicalcondition preferably includes an assertion of the presence or lack of aneurological impairment in the patient, and Block S240 preferablypresents this assertion to the patient or a parent, teach, physician,guardian, employer, etc. of the patient in the form of a recommendeddiagnosis. In one example implementation, Block S240 transmits thediagnosis directly to the patient in the form of a notification within anative application executing on a smartphone carried by the patient. Inthis example implementation, the preferred method can supplement thediagnosis with a recommendation for living with or improving a diagnosedneurological condition. In another example implementation, Block S240uploads the diagnosis to a digital medical record of the patient (e.g.,stored on a hospital server), wherein the medical record is accessibleby a physician of the patient.

The patient EEG signals, synchronization pattern trends, and/ordiagnosis can be fed back into the EEG dataset of Block S110 of themethod S100 to grow and further inform the neurological impairment modelof the method S100.

As shown in FIG. 4 , a variation of the method S200 includes Block S260,which recites mapping a first neural connectivity of the patient basedupon a first patient EEG signal recorded on a first date. Block S260preferably maps the first neural connectivity of the patient in a mannersimilar to Block S160 of the method S100. In this variation, the firstEEG signal preferably includes multiple EEG subsignals, and each EEGsubsignal is preferably associated with neuronal electrical activity ofthe patient sensed at or across a particular location of the patient’sscalp by one or more electrodes of known arrangement on a neuroheadset,as described above.

As shown in FIG. 4 , a variation of the method S200 further includesBlock S270, which recites identifying a change in neural connectivity ofthe patient by comparing the first neural connectivity on the first datewith a second neural connectivity of the patient based upon EEGsubsignals of a second EEG signal recorded on a second (later) date.Block S270 therefore preferably functions as described above. In thisvariation, Block S240 can additionally or alternatively diagnose thepatient based upon the identified change in neural connectivity betweenthe first and second dates. For example, Block S270 can identify anabnormality of neural connectivity of the patient, and Block S240 candiagnose the patient based upon a comparison of the neural connectivityabnormality of the patient with a neural connectivity model extractedfrom EEG data of multiple users of known neurological status, such as aneural connectivity model generated through the method S100.Alternatively, the Block S270. can track patient neural developmenttrends based upon the identified change in patient neural connectivity.Trends in neural development can indicate progression or regression of apreviously-diagnosed disorder, can suggest future therapy programs forthe patient, and/or can inform future interactions with the patient(e.g., patient interactions with a teacher, parent, or coworker).However, Block S260 can function in any other way to output any otherform of neural connectivity to enable any other diagnosis or inform anyother interaction with the patient.

As shown in FIG. 4 , a variation of the method S200 includes Block S250,which recites collecting an output of a physiological or environmentalsensor proximal a user during recordation of an EEG signal, determiningan action of the user based upon the output of the physiological orenvironmental sensor, and tagging the EEG signal with the determinedaction. Block S250. can additionally or alternatively determine anenvironmental condition proximal the patient during EEG signalrecordation, a mood of the user, a physiological status of the user, orany other suitable information relevant to a recorded EEG signal.Therefore, Block S250 preferably identifies the action, environmentalcondition, etc. in a manner similar to Block S150 of the method S100.

In one example implementation, the second preferred method includes:receiving information over a distributed network based upon a series ofEEG signals from a patient; computing nonlinear synchronization patternsfrom the information; comparing trends of the patterns of the patientwith the trends of the patterns from a user with a neurologicalimpairment; and remotely detecting patient development of a neurologicalimpairment based upon the comparison. The series of patient EEG signalspreferably includes multiple EEG signals recorded over a long timeperiod, such as on a daily basis for several months or on a weekly basisfor several years. The extended EEG series preferably facilitatescorrelation of the EEG signals with patient development of aneurological impairment. The patient EEG signals in the series arepreferably collected by the same device (or at least the same type ofdevice with the same settings) and under similar situations (or at leastunder a categorized situation based upon the situation information, asdescribed above). In this example implementation, the method S200 canalso function to provide feedback to the patient or associated entityfor the abatement or reversal of the development of the neurologicalimpairment.

In another example implementation, the method S200 includes: receivinginformation over a distributed network based upon EEG signals from apatient; computing a synchronization pattern from the information;comparing the patient synchronization pattern with a synchronizationpattern of a user recorded during an epileptic seizure; and remotelymonitoring the presence of an epileptic seizure in the user based uponthe comparison. In this example implementation, the method S200 cancommunicating a patient diagnosis, such as for epilepsy, to a heath careprofessional associated with the patient or to any other suitable personor entity. This communication method can be based upon measured ordetermined situation information, as described above. For example, ifthe patient is known to be physically located within a health carefacility or the his home, the communication can be directed to thehealth care facility or to the patient’s family. Furthermore, if thepatient is known to be physically located outside of a designated area,the communication can be directed to an emergency response unit.

The method S200 can further function to remotely treat development of adiagnosed patient neurological impairment. For example, the method S200can communicate a video output (such as via a display or a projection),an audio output (such as via a speaker), a haptic output (such as via avibration device), or any suitable feedback to the patient or anassociated entity (e.g., doctor, souse, parent, teacher, or guardian)for aid in abatement or reversal of the neurological impairment. Inanother example, the method S200 reacts to the occurrence of anepileptic seizure in the patient, such as by communicating a videooutput, an audio output, a haptic output, or any other suitable feedbackfor minimizing patient risk during or following the epileptic seizure.The method S200 can additionally or alternatively initiate emergencycontact or an emergency response based upon patient development of aneurological impairment. The method S200 can also manipulate heavymachinery control interfaces, such as a vehicle braking system and avehicle steering system, to override patient inputs during aneurological episode, such as an epileptic seizure or stroke. However,the method S200 can function in any other way to fulfill any otherdesired function.

3. Tagging EEG Signals

As shown in FIG. 5 , a method S300 for tagging EEG signals includescollecting an EEG signal of a user in Block S310; collecting an outputof a physiological or environmental sensor proximal the user duringrecordation of the EEG signal in Block S320; determining an action ofthe user based upon the output of the physiological or environmentalsensor in Block S330.; and tagging the EEG signal with the determinedaction in Block S340.

The method S300 functions to automatically identify an action of a userduring recordation of an EEG signal by analyzing an output of aphysiological or environmental sensor mounted on, carried by, orarranged proximal a user during recordation of the EEG signal. In oneexample, the physiological or environmental sensor is an accelerometer,a gyroscope, a camera, or a microphone incorporated into a neuroheadsetor a smartphone carried by the user. In another example, thephysiological or environmental sensor is a blood oximeter, a heart ratesensor, an EKG machine, a respiratory sensor, a blood pressure sensor,or any other standalone biometric sensor or biometric sensorincorporated into a neuroheadset or digital multimedia device carried bythe user. However, the physiological or environmental sensor can be anyother suitable sensor generating any other suitable output. The methodS300 therefore preferably functions to automatically tag an EEG signalwith highly-relevant user or environmental information that maysignificantly affect neurological activity and therefore an EEG signalof the user recordation. The method S300 therefore preferably requireslittle or no direct user input to generate action or environmentalcondition tags that can inform grouping or filtering of user EEG signalswith EEG signals of other users and/or with a neurological impairmentmodel. The method S300 may therefore significantly improve the accuracyof a neurological impairment model generated in the method S100. ordiagnosis of a neurological condition of a patient in the method S200.

The method S300 is preferably implemented by the computer system thatalso implements the method S100 and/or the method S200.For example, asensor output contemporaneous with an EEG signal can be communicated tothe computer system along with the EEG signal, wherein the computersystem analyzes the sensor output, determines a user action or status,and tags the EEG signal with the determined user action or status. Thecomputer system can additionally or alternatively tag the EEG signalwith a determined environmental or situational information, as describedabove, including the case where the user’s activities and/or specificstandardized stimuli can be directed or presented by the said computersystem Alternatively, the neuroheadset can communicate EEG signals to adigital multimedia device, such as a smartphone or tablet, and a nativeapplication executing on the digital multimedia device can access asensor signal, extract a user action or environmental condition from thesensor signal, and tag a contemporaneous EEG signal with the determineduser action or environmental condition prior to communicating the EEGsignal to the computer system. Therefore, the method S300 can beimplemented locally, such as on a digital multimedia device carried bythe user, or remotely, such as on a computer system.

Block S310 of the method S300 recites collecting an EEG signal of a userin Block S310. Block S310 preferably collects one or more user EEGsignals in a manner similar to Block S110 of the method S100 and/orBlock S210 of the method S200.

Block S320 of the method S300 recites collecting an output of aphysiological or environmental sensor proximal the user duringrecordation of the EEG signal. As described above, Block S320 canlocally or remotely collect an output of any physiological orenvironmental sensor that is a standalone sensor or that is incorporatedinto a neuroheadset, a digital multimedia device carried by the user, ora local digital device (e.g., thermostat, floor traffic sensor).

Block S330 of the method S300 recites determining an action of the userbased upon the output of the physiological or environmental sensor inBlock S330. Block S330 preferably analyzes the sensor signal andcorrelates the sensor signal with a particular action or condition ofthe user, such as described in Block S150 and Block S250 above. BlockS330 can further analyze signals from multiple sensors to determine theuser action or condition, such as a heart rate sensor and a GPS sensorpair, an accelerometer and a microphone pair, or a thermometer, apedometer, and a light sensor set. Block S330 preferably includesaccessing action models including markers or fingerprints of known orcommon actions such that a sensor signal correlated with a particularaction can be paired with a user EEG signal with a suitable degree ofcertainty. However, Block S330 can function in any other way todetermine an action of the user or environmental condition.

Block S340 of the method S300 recites tagging the EEG signal with thedetermined action. The action and/or environmental tag can be added tothe EEG signal locally or remotely, such as on a smartphone carried bythe user or on a computer system. As described above, the tag preferablyinforms filtering and/or grouping of EEG signals across multiple userswhen generating or growing the neurological impairment model, such asthrough the method S100. Additionally or alternatively, the tag caninform filtering and/or grouping of EEG signals when diagnosing apatient, such as by comparing patient EEG signals or synchronizationpattern trends with a neurological impairment model in the method S200.However, the action and/or environmental tag generated and added to anEEG signal in the method S300 can be used in any other way.

The systems and methods of the preferred embodiments can be embodiedand/or implemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions are preferably executed by computer-executable componentspreferably integrated with the application, applet, host, server,network, website, communication service, communication interface,hardware/firmware/software elements of a user computer or digitalmultimedia (mobile) device, or any suitable combination thereof. Othersystems and methods of the preferred embodiment can be embodied and/orimplemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions are preferably executed by computer-executable componentspreferably integrated by computer-executable components preferablyintegrated with apparatuses and networks of the type described above.The computer-readable medium can be stored on any suitable computerreadable media such as RAMs, ROMs, flash memory, EEPROMs, opticaldevices (CD or DVD), hard drives, floppy drives, or any suitable device.The computer-executable component is preferably a processor but anysuitable dedicated hardware device can (alternatively or additionally)execute the instructions.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A method, comprising: for each user in a set of users: at aneuroheadset associated with the user, recording a set of EEG signals;and for each EEG signal of the set of EEG signals, determining a set ofsynchronization pattern features based on the EEG signal; for the set ofusers: organizing the sets of synchronization pattern features into aset of groups; and assigning a tag to each of the set of groups, the tagassociated with a user state; and determining a model for each group ofthe set of groups, wherein the model is configured to determine apredicted tag for a new user.
 2. The method of claim 1, furthercomprising, for the new user: at a neuroheadset associated with the newuser, recording a new EEG signal; determining a set of new usersynchronization pattern features based on the new EEG signal; comparingthe set of new user synchronization pattern features with the set ofmodels; determining a set of correlations based on comparing the set ofnew user synchronization pattern features with the set of models; anddetermining the predicted tag based on the set of correlations.
 3. Themethod of claim 2, further comprising recommending an activity for thenew user to perform based on the predicted tag.
 4. The method of claim1, wherein determining the model for each group in the set of groupscomprises aggregating the sets of synchronization pattern featuresacross the group.
 5. The method of claim 4, wherein aggregating the setsof synchronization pattern features comprises clustering synchronizationpattern features using machine learning methods.
 6. The method of claim4, wherein aggregating the sets of synchronization pattern featurescomprises isolating common synchronization pattern features.
 7. Themethod of claim 1, wherein the tag comprises a neurological status tag.8. The method of claim 1, wherein, for each user in a set of users, thesets of synchronization pattern features comprise a timeseries.
 9. Themethod of claim 1, wherein, for each of the set of groups, the userstate associated with the tag is determined based on at least one of: auser input or a measurement received from a sensor.
 10. The method ofclaim 9, wherein the sensor comprises at least one of a physiological orenvironmental sensor.
 11. A method, comprising for each user of a set ofusers: at each time point of a set of time points, at a neuroheadset ofthe user, recording an EEG signal; and determining a synchronizationpattern trend based on the EEG signals; and for the set of users:organizing the synchronization pattern trends into a set of groups; andassigning a tag to each of set of groups, the tag associated with a userstate; and determining a model for each group of the set of groups,wherein the model is configured to determine a predicted tag for a newuser.
 12. The method of claim 11, further comprising, for the new user:at each time point of a new set of time points, at a neuroheadset of thenew user, recording a new EEG signal; determining a new synchronizationpattern trend based on the new EEG signals; determining a set ofcorrelations based on the new user synchronization pattern trend and theset of models; and determining the predicted tag based on the set ofcorrelations.
 13. The method of claim 11, wherein determining asynchronization pattern trend based on the EEG signals comprises:extracting a set of features from the EEG signals; and determining thesynchronization pattern trend based on the set of features.
 14. Themethod of claim 11, wherein the user state is associated with a focusedtask.
 15. The method of claim 11, further comprising, for the new user:at each time point of a new set of time points, at a neuroheadset of thenew user, recording a new EEG signal; determining a new synchronizationpattern trend based on the new EEG signals; adding the newsynchronization pattern trend to a group of the set of groups; andupdating the model associated with the group based on the new usersynchronization pattern trend.
 16. The method of claim 15, wherein themodel is updated using machine learning methods.
 17. The method of claim11, wherein the tag comprises a neurological status tag.
 18. The methodof claim 11, wherein determining the synchronization pattern trend foreach user of the set of users comprises: filtering a first EEG signal ofthe EEG signals of the user in a first band and in a second band togenerate a first band-filtered EEG signal and a second band-filtered EEGsignal; and extracting a set of phase locking episodes between the firstband-filtered EEG signal and the second band-filtered EEG signal. 19.The method of claim 11, wherein determining the synchronization patterntrend for each user of the set of users comprises: generating a filteredfirst EEG signal and a filtered second EEG signal upon filtering each ofa first and a second EEG signal of the EEG signals of the user;analyzing the filtered first and second EEG signals via spectralanalysis; and identifying stable phase difference episodes between thefiltered first and second EEG signals via statistical identification ofphase-locking synchrony.
 20. The method of claim 11, wherein thesynchronization pattern trend is nonlinear.